Machine Learnability as a Measure of Order in Aperiodic Sequences
4 days ago
- #machine-learning
- #primes
- #number-theory
- Machine learning models can measure regularity in prime number distributions on Ulam spirals.
- Models trained on higher regions (around 500m) outperform those trained on lower regions (below 25m), indicating more learnable order.
- Classification strategies differ: lower regions focus on identifying primes, while higher regions prioritize eliminating composites.
- Findings align with number theory, suggesting reduced noise and more predictable patterns at higher magnitudes.
- Machine learning could serve as an experimental tool for number theory, especially in studying prime patterns for cryptography.